Digital image editing is used to change the perceived appearance of objects within a scene. These changes may entail adjusting the contrast, or gamma, or any global characteristic, across the entire image. Additionally, the aforementioned changes may entail manipulating the color or brightness of individual objects. To change the appearance of a specific object or a portion of the scene, including highlights or shadows, the relevant pixels need to be identified. This also includes finding pixels that only contain a faction of the object or region of interest. For example, pixels near object boundaries may receive contribution from multiple objects, or pixels at shadow boundaries might only be partially shaded. The identification of the various sources contributing to a pixel is known as matting.
Standard matting approaches are object centric, in that the matting information is computed in a narrow region around a user identified object. Several matting techniques exist for finding object boundaries for use in object extraction and insertion. However, none of these techniques consider finding complete matting information across the entire image.
Another digital image manipulation topic that has been extensively studied is that of noise estimation and removal. Various techniques have been devised to estimate and remove noise from digital images, such as wavelet techniques, bi-lateral filtering and anisotropic smoothing. Here as well, matting is used to identify the pixels that are to undergo noise reduction, but none of the existing noise reduction techniques consider finding complete matting information across the entire image.